We describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. We express temperature and precipitation as simple functions of the past trajectory of atmospheric CO2 concentrations and fit a statistical model using a limited set of training runs. We demonstrate that the approach is a useful and computationally efficient alternative to pattern scaling that captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.